Introduction:

GGCHEMPY: Gas-Grain CHEMical code for interstellar medium in Python3.

Author: Jixing Ge

E-mail: gejixing666@gmail.com

Required Python Verion:

> Python 3.0

Required packages:

numba
numpy
scipy
matplotlib
progressbar
platform
pyqt5                 -> for using GUI
networkx              -> for using Analysis_tool.py in GUI
pyfiglet (optional)   -> to create ASCII art text of "GGCHEM"

Usage:

<1> install GGCHEM:
python setup.py build
python setup.py install
<2> Prepare your models.
<3> Run models. 

For example, please type the following command on your terminal:

Python benchmark.py

Analysis tool

Analysis_tool.py provides a GUI to analyze a given species at a age. See GIF.

Feature: each species in the textlist is clickable to be analyzed.

Just type "python Analysis_tool.py" on your termical and press "ENTER" to use it.

Benchmark with the five models of Semenov et al., (2010):

Solid line: GGCHEMPY

Point: model of Semenov et al., (2010).

CPU times (s):

Note:

There is a file "in/network2.txt" with updated reactions of HNCO.

Reactive desorption is only available for this updated reaction network since that enthalpies taken from KIDA database were only added to it.

For citing GGCHEMPY:

https://iopscience.iop.org/article/10.1088/1674-4527/ac321e

https://ui.adsabs.harvard.edu/abs/2021arXiv211011117G/abstract

The bibtex

@article{Ge_2022, doi = {10.1088/1674-4527/ac321e}, url = {https://doi.org/10.1088/1674-4527/ac321e}, year = 2022, month = {jan}, publisher = {{IOP} Publishing}, volume = {22}, number = {1}, pages = {015004}, author = {Jixing Ge}, title = {{GGCHEMPY}: A Pure Python-based Gas-grain Chemical Code for Efficient Simulation of Interstellar Chemistry{\ast}}, journal = {Research in Astronomy and Astrophysics}, abstract = {In this paper, we present a new gas-grain chemical code for interstellar clouds written in pure Python (GGCHEMPY (GGCHEMPY is available on https://github.com/JixingGE/GGCHEMPY)). By combining with the high-performance Python compiler Numba, GGCHEMPY is as efficient as the Fortran-based version. With the Python features, flexible computational workflows and extensions become possible. As a showcase, GGCHEMPY is applied to study the general effects of three-dimensional projection on molecular distributions using a two-core system which can be easily extended for more complex cases. By comparing the molecular distribution differences between two overlapping cores and two merging cores, we summarized the typical chemical differences such as N2H+, HC3N, C2S, H2CO, and C2H, which can be used to interpret 3D structures in molecular clouds.} }

References:

<1> for basic rate equation method:
Hasegawa T. I., Herbst E., Leung C. M., 1992, ApJS, 82, 167
Semenov D., et al., 2010, A&A, 522, A42

<2> for reactive desorption:
Garrod R. T., Wakelam V., Herbst E., 2007, A&A, 467, 1103
Minissale M., Dulieu F., Cazaux S., Hocuk S., 2016, A&A, 585, A24

<3> for GGCHEM in Fortran:
Ge J. X., He J. H., Yan H. R., 2016, MNRAS, 455, 3570
Ge J. X., He J. H., Li A., 2016, MNRAS, 460, L50
Ge J., Mardones D., Inostroza N., Peng Y., 2020, MNRAS, 497, 3306
Ge J. X., et al., 2020, ApJ, 891, 36

<4> for reaction network and benchmark:
Semenov D., et al., 2010, A&A, 522, A42

Acknowledgements

This work is accomplished with the support from the Chinese Academy of Sciences (CAS) through a Postdoctoral Fellowship administered by the CAS South America Center for Astronomy (CASSACA) in Santiago, Chile.

Jixing thanks Dr. Jinhua He and Dr. Tie Liu for their constructive suggestions on improving the code and the paper

Links:

KIDA database

UDFA database

GitFront page